OP0223 DISTINGUISHING OSTEOARTHRITIS AND RHEUMATOID ARTHRITIS SYNOVIUM WITH MACHINE LEARNING USING AUTOMATED CELL DENSITY AND PATHOLOGIST SCORES
Authors: B. Mehta, S. Goodman, E. Dicarlo, D. Jannat-Khah, J. A. Gibbons, M. Otero, L. Donlin, T. Pannellini, W. Robinson, P. Sculco, M. Figgie, J. Rodriguez, J. Kirschmann, J. Thompson, D. Slater, D. Frezza, Z. Xu, F. Wang, D. Orange
Journal: Annals of the Rheumatic Diseases (2022)

Background Joint damage in the knee can be severe in both rheumatoid arthritis (RA) and osteoarthritis (OA) such that total knee replacement (TKR) is often the only management option. Pathological assessment of the extent or type of synovial tissue inflammation from joint explants or biopsies can be useful. However, an ongoing challenge in using semi-quantitative assessments of synovitis is the disagreement between human pathologist scores of the same sample. We previously developed and validated a computer vision algorithm to automatically count each cell nucleus in an H&amp;E-stained synovial whole slide image and yield a value of cell density, defined as mean nuclei count per mm 2 of tissue 1 . Objectives We sought to develop methods to distinguish OA from RA based on machine learning analysis of histologic features on H&amp;E-stained synovial tissue samples. Methods We measured 14 pathologist-scored histology features (137 RA and 152 OA patients) and computer vision quantified cell density (60 RA and 147 OA patients) in H&amp;E stained synovial tissue samples from total knee replacement arthroplasty explants. A random forest model was trained using disease state (OA vs RA) as classifier and histology features and/or cell density as inputs, and feature importance scores for the model were calculated. Results Synovium from patients with RA exhibited increased lymphocytic inflammation, lining hyperplasia, neutrophils, detritus, plasma cells, Russell bodies, binucleate plasma cells, sub-lining giant cells, synovial lining giant cells, and fibrin (all p&lt;0.001), while synovium from patients with OA had increased mast cells and fibrosis (both p&lt;0.001). Fourteen pathologist-scored features allowed for discrimination between RA and OA samples, producing a macro-averaged area under the receiver operating curve (AUC) of 0.85. This discriminatory ability was comparable to that of the computer vision score of cell density alone (AUC = 0.88). Combining the pathologist scores with the cell density metric improved the discriminatory power of the model (AUC = 0.91). The three most important features in this combined model were mast cells followed by cell density and fibrosis (Figure 1). AUC values for each individual feature are provided in Table 1. The optimal cell density threshold to distinguish RA from OA synovium was 3,400 cells per mm 2 , which yielded a sensitivity of 0.82 and specificity of 0.82. Table 1. Area under receiver operating characteristic curves (AUC) of the synovial features in distinguishing RA and OA patients Feature AUC Automated Cell Density 0.88 Fibrosis 0.84 Mast cells 0.80 Lining hyperplasia 0.78 Lymphocytic inflammation 0.69 Fibrin 0.68 Plasma cells 0.66 Detritus 0.64 Binucleate plasma cells 0.60 Neutrophils 0.60 Synovial giant cells 0.58 Sub-lining giant cells 0.57 Russell bodies 0.56 Germinal centers 0.51 Mucoid change 0.50 Figure 1. Importance of synovial features in distinguishing RA and OA synovium Feature importance scores for supervised machine learning model including all 14 pathology scores and the computer vision-generated cell density. Conclusion H&amp;E-stained images of RA and OA TKR explant synovium are distinct. We identified cell density, mast cells and fibrosis as the three most important features for making this distinction, with RA being characterized by increased cell density, low mast cells, and low fibrosis. Cell density greater than 3400 per mm 2 of tissue yields a sensitivity of 0.82 and a specificity of 0.82 for distinguishing RA from OA. In the future, this can have clinical and research applications as this technique removes the requirement for subjective selection of a certain field of interest, is reproducible, and is scalable as it does not require technical expertise of a pathologist. References [1]Guan S, Mehta B…Orange DE. Rheumatoid Arthritis Synovial Inflammation Quantification Using Computer Vision. ACR Open Rheumatology. 2022 Jan 10;acr2.11381. Acknowledgements This work was supported by the C. Ronald MacKenzie Young Scientist Endowment Award, the Leon Lowenstein Foundation, and the Kellen Scholar Award supported by the Anna Marie and Stephen Kellen Foundation Total Knee Improvement Program. Disclosure of Interests Bella Mehta Paid instructor for: Novartis, Susan Goodman Consultant of: UCB, Grant/research support from: Novartis, Edward DiCarlo: None declared, Deanna Jannat-Khah Shareholder of: AstraZeneca, Cytodyn, and Walgreens, J. Alex Gibbons: None declared, Miguel Otero Consultant of: Regeneron Pharmaceuticals, Grant/research support from: Tissue Genesis, Laura Donlin Speakers bureau: Stryker, Consultant of: Stryker, Grant/research support from: Karius, Inc, Tania Pannellini: None declared, William Robinson: None declared, Peter Sculco Consultant of: Intellijoint Surgical, DePuy Synthes, Lima Corporate, Zimmer Biomet, and EOS Imaging, Grant/research support from: Intellijoint Surgical and Zimmer Biomet, Mark Figgie Shareholder of: HS2, Mekanika, and Wishbone, Consultant of: Lima and Wishbone, Jose Rodriguez Consultant of: ConforMIS, Medacta, Exactech, Inc, and Smith &amp; Nephew, Grant/research support from: DePuy, Exactech, Inc, and Smith &amp; Nephew, Jessica Kirschmann: None declared, James Thompson: None declared, David Slater: None declared, Damon Frezza: None declared, Zhenxing Xu: None declared, Fei Wang: None declared, Dana Orange: None declared